The need to evaluate economic asset management options for nuclear power plants is important with the increased reliance on nuclear power plants for energy production. To aid in evaluating the management options, individual utilities, as well as the nuclear industry in general, have developed extensive databases describing various aspects of steam generator maintenance to allow operators to predict the economic effects of various combinations of degradation mechanisms coupled with different asset maintenance scenarios for those degradation mechanisms. The needs of the nuclear industry, however, clearly indicate that there is no presently available methodology, using collected field data, to allow a credible assessment of asset management scenario options for maintaining the steam generators of existing nuclear power plant stations.
The nuclear industry has operated for a number of years with predictive tools that have been continuously improved, these improvements benefiting from larger defect database populations and better mathematical tools. These tools have been the initial elements in any economical evaluations of nuclear steam generator corrective action alternatives.
With increasing knowledge of the types and processes for deposition of materials as determined from mass balance equations during successive cycles of steam generator operation, the prediction of deposition and the effects of such deposition has been continuously improved. With the expansion of various investigative techniques used in monitoring and assuring steam generator readiness for service, the number of databases and the complexity of data available for evaluation has increased tremendously, making any attempt to accurately provide steam generator asset management an expensive and time consuming exercise. The aggregation of the large number of available parameters pertaining to steam generator status and the selection of the most significant or useful parameters to evaluate has increased the difficulty of efficiently producing a credible economic analysis of steam generator asset management options.
In addition to maintaining the growing databases needed, there is a continuous effort to maintain a level of quality of the descriptive indicators pertaining to steam generator tubing features.
Currently, there is a conventional methodology to assess the cleanliness condition of a steam generator. This conventional methodology brings together operational and inspection data, such as thermal performance and water chemistry data. The cleanliness condition of the tubes of the steam generator is quantified in terms of a “fouling index”. The “fouling index” allows for the monitoring of the condition of a specific steam generator, comparing it to other steam generators in the same plant or with steam generators in other plants of a like design. The “fouling factor” also serves as a criterion for selecting future asset management measures such as chemical cleaning.
The first step of this conventional methodology is to select a number of variables for evaluation. The variables used are provided Table 1. Data is then collected during a nuclear refueling outage (e.g. the height of the sludge on steam generator tubesheet is measured), or during the cycle before an outage (e.g. chemistry data) for each of the variables. In the next step of the conventional methodology, weighting factors are applied to each of the five variables; heat transfer performance, water chemistry parameters, sludge on tubesheet, last tubesheet sludge lancing, and tube scale measurements. Note that each of these major variables has a number of variables of interest from which the major indicator is developed. The sum of all weighting factors is 100% for the nuclear steam generator. Within each main category, however, the indicator variables are individually weighted.
TABLE 1Weighting of Fouling IndicatorsiIndicator VariablesWeighting FactorsHeat transfer performance30%1Fouling factor. . .2Heat transfer margins. . .3Growth rate. . .. . .. . .. . .Water chemistry parameters30%N2H4-ratio. . .Fe inventoiy. . .Hide-out behaviour. . .Hide-out-return behaviour. . .. . .. . .Sludge on tube sheet15%Number of tubes affected. . .Height of sludge. . .Colours of tube scale. . .. . .. . .Last TS lancing15%Quantity removed. . .Density of sludge. . .Composition of sludge. . .. . .. . .Tube scale measurements10%Scale thickness. . .Appearance, colours. . .n. . .. . .100% Sum
An overall “fouling index” of the steam generator is obtained from the sum of the weighted individual indicator variables This “fouling index”, as per the above definitions, is between 0 and 100, where 0 stands for “clean” and 100 stands for “fouled.”
According to the conventional methodology, three zones are defined for the fouling index and for the actions to be taken in each zone [1]:                “Green”, index 0 to 50: No cleaning actions are required.        “Orange”, index 50 to 80: An optimization of the chemistry program should be considered (corrosion product control, oxygen control etc.) and cleaning measures should be planned in the long term.        “Red”, index 80 to 100: cleaning actions should be initiated as soon as possible. Cleaning actions are defined here as mechanical tubesheet lancing with high pressure water jets, bundle deluge flushing or chemical cleaning of the whole tube bundle.        
This conventional methodology selects, as a synthetic indicator, a value obtained from combining a number of databases using for each one an “experience based” weighting factor.
To establish confidence in the “fouling index”, researchers need to acquire empirical data to provide a basis for discerning between various results. Without this data, the index is not a meaningful basis for conducting decisions on spending economic resources to remedy identified problems.
To accomplish this evaluative process accurately, the “fouling index” for many plants, for various points in time, must be calculated. For each of the plants, researchers need to correlate operating experiences (measured in terms of operating and maintenance cost, or onset of corrosion, or power loss, etc.) with the index. Assuming there is a correlation, the index would then be meaningful.
There are several defects with this type of methodology for evaluation of steam generator assets and the methodologies to be used to protect those assets. First, the conventional methodology is not clear as to what basis is used to change the weighting factors or fouling index action levels. For example, it is unknown what type of fouling index would trigger use of the techniques for cleaning and how such cleaning techniques would renormalize other indices.
It is also not clear how variables, such as the measurable variables (see Table 1), for the last tubesheet lancing indicator are aggregated to obtain the values for that process. The current methodologies do not account for introducing another measured characteristic variable, such as the proportion of the tubesheet cleaned during sludge lancing and if so, how would these variables be combined for the overall last tubesheet lancing indicator variable.
The conventional methodology does not disclose or suggest the capability of replacing any variables with “equivalent variables”. For example, it could be desirable to use final feedwater oxygen flow in evaluative calculations instead of a hydrazine ratio. Additionally, the conventional methodology does not discuss using a net deposit inventory for any evaluative purposes.
The conventional methodology does not account for pressure margin reduction or power loss variables, significantly impacting evaluative analysis. The conventional methodology is also too rigid. Introduction of new measurements or elimination of variables that have no reliable history are not addressed. There is no provision for renormalization of the measurement weighting factors required to maintain the validity of the required action trigger levels.
There is therefore a need to provide a methodology to account for pressure margin reduction or power loss variables to allow proper analysis of nuclear steam generator condition and corrective action alternatives.
There is also a need to provide a methodology that will allow for renormalization of measurement weighting factors to maintain the validity of required corrective action trigger levels for nuclear steam generators.
There is also a need to provide a methodology that will allow the introduction of other measured characteristic variables for evaluation, rather than having a static set of evaluation parameters.